OpenRaiser/Intern-Atlas huggingface dataset:
Vectorized Calculation of Short Circuit Currents Considering Distributed Generation - An Open Source Implementation of IEC 60909
Venue: arXiv cs.CE 2018
Year: 2018

An important task in grid planning is to ensure that faults in the grid are detected and cut off without damage in any grid element. Calculating short circuit currents is therefore a vital grid analysis functionality for grid planning applications. The standard IEC 60909 provides guidelines for short circuit calculations and is routinely applied in grid planning applications. This paper presents a method for the vectorized calculation of short circuit currents according to IEC 60909. Distributed generation units are considered according to the latest revision of the standard. The method is implemented in the python based open source tool pandapower and validated against commercial software and examples from literature. The implementation presented in this paper is the first comprehensive implementation of the IEC 60909 standard which is available under an open source license. It can be used to evaluate fault currents in grid studies with a high degree of automation and is shown to scale well for large grids. Its practical applicability is shown in a case study with a real MV grid with a high degree of DG penetration.
Covariance Matrix Estimation for Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

We propose a novel pilot structure for covariance matrix estimation in massive multiple-input multiple-output (MIMO) systems in which each user transmits two pilot sequences, with the second pilot sequence multiplied by a random phase-shift. The covariance matrix of a particular user is obtained by computing the sample cross-correlation of the channel estimates obtained from the two pilot sequences. This approach relaxes the requirement that all the users transmit their uplink pilots over the same set of symbols. We derive expressions for the achievable rate and the mean-squared error of the covariance matrix estimate when the proposed method is used with staggered pilots. The performance of the proposed method is compared with existing methods through simulations.
Supporting UAV Cellular Communications through Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

In this article, we provide a much-needed study of UAV cellular communications, focusing on the rates achievable for the UAV downlink command and control (C&C) channel. For this key performance indicator, we perform a realistic comparison between existing deployments operating in single-user mode and next-generation multi-user massive MIMO systems. We find that in single-user deployments under heavy data traffic, UAVs flying at 50 m, 150 m, and 300 m achieve the C&C target rate of 100 kbps -- as set by the 3GPP -- in a mere 35%, 2%, and 1% of the cases, respectively. Owing to mitigated interference, a stronger carrier signal, and a spatial multiplexing gain, massive MIMO time division duplex systems can dramatically increase such probability. Indeed, we show that for UAV heights up to 300 m the target rate is met with massive MIMO in 74% and 96% of the cases with and without uplink pilot reuse for channel state information (CSI) acquisition, respectively. On the other hand, the presence of UAVs can significantly degrade the performance of ground users, whose pilot signals are vulnerable to UAV-generated contamination and require protection through uplink power control.
The Matrix Calculus You Need For Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don't worry if you get stuck at some point along the way---just go back and reread the previous section, and try writing down and working through some examples. And if you're still stuck, we're happy to answer your questions in the Theory category at forums.fast.ai. Note: There is a reference section at the end of the paper summarizing all the key matrix calculus rules and terminology discussed here. See related articles at http://explained.ai
Expressive Robot Motion Timing
Venue: arXiv cs.RO 2018
Year: 2018

Our goal is to enable robots to \emph{time} their motion in a way that is purposefully expressive of their internal states, making them more transparent to people. We start by investigating what types of states motion timing is capable of expressing, focusing on robot manipulation and keeping the path constant while systematically varying the timing. We find that users naturally pick up on certain properties of the robot (like confidence), of the motion (like naturalness), or of the task (like the weight of the object that the robot is carrying). We then conduct a hypothesis-driven experiment to tease out the directions and magnitudes of these effects, and use our findings to develop candidate mathematical models for how users make these inferences from the timing. We find a strong correlation between the models and real user data, suggesting that robots can leverage these models to autonomously optimize the timing of their motion to be expressive.
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as the adversarial attack. Many defense methodologies have been investigated to defend against such adversarial attack. In this work, we propose a novel methodology to defend the existing powerful attack model. We for the first time introduce a new attacking scheme for the attacker and set a practical constraint for white box attack. Under this proposed attacking scheme, we present the best defense ever reported against some of the recent strong attacks. It consists of a set of nonlinear function to process the input data which will make it more robust over the adversarial attack. However, we make this processing layer completely hidden from the attacker. Blind pre-processing improves the white box attack accuracy of MNIST from 94.3\% to 98.7\%. Even with increasing defense when others defenses completely fail, blind pre-processing remains one of the strongest ever reported. Another strength of our defense is that it eliminates the need for adversarial training as it can significantly increase the MNIST accuracy without adversarial training as well. Additionally, blind pre-processing can also increase the inference accuracy in the face of a powerful attack on CIFAR-10 and SVHN data set as well without much sacrificing clean data accuracy.
Can One Escape Red Chains? Regular Path Queries Determinacy is Undecidable
Venue: arXiv cs.DB 2018
Year: 2018

For a given set of queries (which are expressions in some query language) $\mathcal{Q}=\{Q_1$, $Q_2, \ldots Q_k\}$ and for another query $Q_0$ we say that $\mathcal{Q}$ determines $Q_0$ if -- informally speaking -- for every database $\mathbb D$, the information contained in the views $\mathcal{Q}({\mathbb D})$ is sufficient to compute $Q_0({\mathbb D})$. Query Determinacy Problem is the problem of deciding, for given $\mathcal{Q}$ and $Q_0$, whether $\mathcal{Q}$ determines $Q_0$. Many versions of this problem, for different query languages, were studied in database theory. In this paper we solve a problem stated in [CGLV02] and show that Query Determinacy Problem is undecidable for the Regular Path Queries -- the paradigmatic query language of graph databases.
Unified Analysis and Optimization of D2D Communications in Cellular Networks Over Fading Channels
Venue: arXiv cs.IT 2018
Year: 2018

This paper develops an innovative approach to the modeling and analysis of downlink cellular networks with device-to-device (D$2$D) transmissions. The analytical embodiment of the signal-to-noise and-interference ratio (SINR) analysis in general fading channels is unified due to the H-transform theory, a taxonomy never considered before in stochastic geometry-based cellular network modeling and analysis. The proposed framework has the potential, due to versatility of the Fox's H functions, of significantly simplifying the cumbersome analysis procedure and representation of D$2$D and cellular coverage, while subsuming those previously derived for all the known simple and composite fading models. By harnessing its tractability, the developed statistical machinery is employed to launch an investigation into the optimal design of coexisting D$2$D and cellular communications. We propose novel coverage-aware power control combined with opportunistic access control to maximize the area spectral efficiency (ASE) of D$2$D communications. Simulation results substantiate performance gains achieved by the proposed optimization framework in terms of cellular communication coverage probability, average D$2$D transmit power, and the ASE of D$2$D communications under different fading models and link- and network-level dynamics.
Robust Watertight Manifold Surface Generation Method for ShapeNet Models
Venue: arXiv cs.CG 2018
Year: 2018

In this paper, we describe a robust algorithm for 2-Manifold generation of various kinds of ShapeNet Models. The input of our pipeline is a triangle mesh, with a set of vertices and triangular faces. The output of our pipeline is a 2-Manifold with vertices roughly uniformly distributed on the geometry surface. Our algorithm uses an octree to represent the original mesh, and construct the surface by isosurface extraction. Finally, we project the vertices to the original mesh to achieve high precision. As a result, our method can be adopted efficiently to all ShapeNet models with the guarantee of correct 2-Manifold topology.
State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities
Venue: arXiv cs.CR 2018
Year: 2018

Nowadays with the help of advanced technology, modern vehicles are not only made up of mechanical devices but also consist of highly complex electronic devices and connections to the outside world. There are around 70 Electronic Control Units (ECUs) in modern vehicle which are communicating with each other over the standard communication protocol known as Controller Area Network (CAN-Bus) that provides the communication rate up to 1Mbps. There are different types of in-vehicle network protocol and bus system namely Controlled Area Network (CAN), Local Interconnected Network (LIN), Media Oriented System Transport (MOST), and FlexRay. Even though CAN-Bus is considered as de-facto standard for in-vehicle network communication, it inherently lacks the fundamental security features by design like message authentication. This security limitation has paved the way for adversaries to penetrate into the vehicle network and do malicious activities which can pose a dangerous situation for both driver and passengers. In particular, nowadays vehicular networks are not only closed systems, but also they are open to different external interfaces namely Bluetooth, GPS, to the outside world. Therefore, it creates new opportunities for attackers to remotely take full control of the vehicle. The objective of this research is to survey the current limitations of CAN-Bus protocol in terms of secure communication and different solutions that researchers in the society of automotive have provided to overcome the CAN-Bus limitation on different layers.
ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks
Venue: arXiv cs.CY 2018
Year: 2018

Cross-institutional healthcare predictive modeling can accelerate research and facilitate quality improvement initiatives, and thus is important for national healthcare delivery priorities. For example, a model that predicts risk of re-admission for a particular set of patients will be more generalizable if developed with data from multiple institutions. While privacy-protecting methods to build predictive models exist, most are based on a centralized architecture, which presents security and robustness vulnerabilities such as single-point-of-failure (and single-point-of-breach) and accidental or malicious modification of records. In this article, we describe a new framework, ModelChain, to adapt Blockchain technology for privacy-preserving machine learning. Each participating site contributes to model parameter estimation without revealing any patient health information (i.e., only model data, no observation-level data, are exchanged across institutions). We integrate privacy-preserving online machine learning with a private Blockchain network, apply transaction metadata to disseminate partial models, and design a new proof-of-information algorithm to determine the order of the online learning process. We also discuss the benefits and potential issues of applying Blockchain technology to solve the privacy-preserving healthcare predictive modeling task and to increase interoperability between institutions, to support the Nationwide Interoperability Roadmap and national healthcare delivery priorities such as Patient-Centered Outcomes Research (PCOR).
On the chordality of polynomial sets in triangular decomposition in top-down style
Venue: arXiv cs.SC 2018
Year: 2018

In this paper the chordal graph structures of polynomial sets appearing in triangular decomposition in top-down style are studied when the input polynomial set to decompose has a chordal associated graph. In particular, we prove that the associated graph of one specific triangular set computed in any algorithm for triangular decomposition in top-down style is a subgraph of the chordal graph of the input polynomial set and that all the polynomial sets including all the computed triangular sets appearing in one specific simply-structured algorithm for triangular decomposition in top-down style (Wang's method) have associated graphs which are subgraphs of the the chordal graph of the input polynomial set. These subgraph structures in triangular decomposition in top-down style are multivariate generalization of existing results for Gaussian elimination and may lead to specialized efficient algorithms and refined complexity analyses for triangular decomposition of chordal polynomial sets.
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
Venue: arXiv cs.RO 2018
Year: 2018

The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human partner from his or her ongoing motion, and re-plans its own actions in real time. This anticipative adaptation is desirable in many practical scenarios, where humans are unable or unwilling to take on the cognitive overhead required to explicitly communicate their intent to the robot. A behavioral experiment indicates that the combination of goal inference and dynamic task planning significantly improves both objective and perceived performance of the human-robot team. Participants were highly sensitive to the differences between robot behaviors, preferring to work with a robot that adapted to their actions over one that did not.
Aggregate Graph Statistics
Venue: arXiv cs.DC 2018
Year: 2018

Collecting statistic from graph-based data is an increasingly studied topic in the data mining community. We argue that these statistics have great value as well in dynamic IoT contexts: they can support complex computational activities involving distributed coordination and provision of situation recognition. We show that the HyperANF algorithm for calculating the neighbourhood function of vertices of a graph naturally allows for a fully distributed and asynchronous implementation, thanks to a mapping to the field calculus, a distribution model proposed for collective adaptive systems. This mapping gives evidence that the field calculus framework is well-suited to accommodate massively parallel computations over graphs. Furthermore, it provides a new "self-stabilising" building block which can be used in aggregate computing in several contexts, there including improved leader election or network vulnerabilities detection.
Channel Diagonalization for Cloud Radio Access
Venue: arXiv cs.IT 2018
Year: 2018

The diagonalization of a conventional multiple-input multiple-output (MIMO) channel into parallel and independent subchannels via singular value decomposition (SVD) is a fundamental strategy that allows the MIMO channel capacity to be achieved using scalar channel codes. This letter establishes a similar channel diagonalization result for the uplink and the downlink of a cloud radio access network (C-RAN), in which a central processor (CP) is connected to a remote radio head (RRH) serving a single user via rate-limit digital fronthaul carrying the compressed baseband signal. Specifically, we show that the diagonalization of the MIMO channel between the RRH and the user via SVD and the subsequent independent and parallel quantization of scalar signals and channel coding in each of the subchannels is optimal. This letter establishes this fact using the majorization theory. Further, an uplink-downlink duality for the multiple-antenna C-RAN is identified for this single-user case.
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often causes much repetition. To address this problem, we propose the decoding-history-based Adaptive Control of Attention (ACA) for the NMT model. ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration. Experiments on Chinese-English translation and the English-Vietnamese translation have demonstrated that our model significantly outperforms the strong baselines. The analysis shows that our model is capable of generating translation with less repetition and higher accuracy. The code will be available at https://github.com/lancopku
A Neurobiologically Motivated Analysis of Distributional Semantic Models
Venue: arXiv cs.CL 2018
Year: 2018

The pervasive use of distributional semantic models or word embeddings in a variety of research fields is due to their remarkable ability to represent the meanings of words for both practical application and cognitive modeling. However, little has been known about what kind of information is encoded in text-based word vectors. This lack of understanding is particularly problematic when word vectors are regarded as a model of semantic representation for abstract concepts. This paper attempts to reveal the internal information of distributional word vectors by the analysis using Binder et al.'s (2016) brain-based vectors, explicitly structured conceptual representations based on neurobiologically motivated attributes. In the analysis, the mapping from text-based vectors to brain-based vectors is trained and prediction performance is evaluated by comparing the estimated and original brain-based vectors. The analysis demonstrates that social and cognitive information is better encoded in text-based word vectors, but emotional information is not. This result is discussed in terms of embodied theories for abstract concepts.
Computing Popov and Hermite forms of rectangular polynomial matrices
Venue: arXiv cs.SC 2018
Year: 2018

We consider the computation of two normal forms for matrices over the univariate polynomials: the Popov form and the Hermite form. For matrices which are square and nonsingular, deterministic algorithms with satisfactory cost bounds are known. Here, we present deterministic, fast algorithms for rectangular input matrices. The obtained cost bound for the Popov form matches the previous best known randomized algorithm, while the cost bound for the Hermite form improves on the previous best known ones by a factor which is at least the largest dimension of the input matrix.
Code Reuse With Transformation Objects
Venue: arXiv cs.PL 2018
Year: 2018

We present an approach for a lightweight datatype-generic programming in Objective Caml programming language aimed at better code reuse. We show, that a large class of transformations usually expressed via recursive functions with pattern matching can be implemented using the single per-type traversal function and the set of object-encoded transformations, which we call transformation objects. Object encoding allows transformations to be modified, inherited and extended in a conventional object-oriented manner. However, the data representation is kept untouched which preserves the ability to construct and pattern-match it in the usual way. Our approach equally works for regular and polymorphic variant types which makes it possible to combine data types and their transformations from statically typed and separately compiled components. We also present an implementation which allows us to automatically derive most functionality from a slightly augmented type descriptions.
A Deep Reinforcement Learning Based Approach for Cost- and Energy-Aware Multi-Flow Mobile Data Offloading
Venue: arXiv cs.NI 2018
Year: 2018

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on to which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues such as different applications' deadlines, monetary cost and energy consumption, how the MU decides whether to offload their traffic to a complementary wireless LAN is an important issue. Previous studies assume the MU's mobility pattern is known in advance, which is not always true. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption without known MU mobility pattern. We propose to use a kind of reinforcement learning technique called deep Q-network (DQN) for MU to learn the optimal offloading policy from past experiences. In the proposed DQN based offloading algorithm, MU's mobility pattern is no longer needed. Furthermore, MU's state of remaining data is directly fed into the convolution neural network in DQN without discretization. Therefore, not only does the discretization error present in previous work disappear, but also it makes the proposed algorithm has the ability to generalize the past experiences, which is especially effective when the number of states is large. Extensive simulations are conducted to validate our proposed offloading algorithms.